
"""simple docstring"""



import functools
from typing import Any


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	bool:





    """simple docstring"""

    if not isinstance(_A       ,   _A   ) or len(_A   ) == 0:
        raise ValueError("""the string should be not empty string"""   )

    if not isinstance(_A       ,   _A   ) or not all(
        isinstance(_A       ,   _A   ) and len(_A   ) > 0 for item in words   ):
        raise ValueError("""the words should be a list of non-empty strings"""   )

    # Build trie
    _UpperCAmelCase        = {}
    _UpperCAmelCase        = """WORD_KEEPER"""

    for word in words:
        _UpperCAmelCase        = trie
        for c in word:
            if c not in trie_node:
                _UpperCAmelCase        = {}

            _UpperCAmelCase        = trie_node[c]

        _UpperCAmelCase        = True

    _UpperCAmelCase        = len(_A   )

    # Dynamic programming method
    @functools.cache
    def is_breakable(_A   ) -> bool:
        if index == len_string:
            return True

        _UpperCAmelCase        = trie
        for i in range(_A       ,   _A   ):
            _UpperCAmelCase        = trie_node.get(string[i]       ,   _A   )

            if trie_node is None:
                return False

            if trie_node.get(_A       ,   _A   ) and is_breakable(i + 1   ):
                return True

        return False

    return is_breakable(0   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

"""simple docstring"""



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Any       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = RobertaPreLayerNormConfig.from_pretrained(
        _A       ,   architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

    # convert state_dict
    _UpperCAmelCase        = torch.load(hf_hub_download(repo_id=_A       ,   filename="""pytorch_model.bin"""   )   )
    _UpperCAmelCase        = {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("""roberta."""   ):
            _UpperCAmelCase        = """roberta_prelayernorm.""" + tensor_key[len("""roberta."""   ) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(""".self.LayerNorm.weight"""   ) or tensor_key.endswith(""".self.LayerNorm.bias"""   ):
            continue

        _UpperCAmelCase        = tensor_value

    _UpperCAmelCase        = RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=_A       ,   config=_A       ,   state_dict=_A   )
    model.save_pretrained(_A   )

    # convert tokenizer
    _UpperCAmelCase        = AutoTokenizer.from_pretrained(_A   )
    tokenizer.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						Tuple       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint-repo''',
     default=None,
     type=str,
     required=True,
     help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Union[str, Any]       		=		parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)